• DocumentCode
    2553553
  • Title

    A graph based transductive ranking algorithm

  • Author

    Pan, Zhibin ; Wei, Xiaoyan

  • Author_Institution
    Coll. of Sci., Huazhong Agric. Univ., Wuhan, China
  • fYear
    2012
  • fDate
    29-31 May 2012
  • Firstpage
    991
  • Lastpage
    994
  • Abstract
    Semi-supervised ranking is a newly developed machine learning problem. In this paper, based on the graph constructed on both labeled and unlabeled data points, we propose a novel semi-supervised ranking algorithm in the transductive setting via a semi-supervised regression model. We also derive the solution in an explicit form for this model. Experiments on two QSAR data sets demonstrate its utility and effectiveness.
  • Keywords
    QSAR; graph theory; learning (artificial intelligence); regression analysis; QSAR data sets; graph construction; graph-based transductive ranking algorithm; labeled data points; machine learning problem; semisupervised ranking; semisupervised regression model; transdutive setting; unlabeled data points; Algorithm design and analysis; Biology; Compounds; Correlation; Laplace equations; Machine learning; Standards; Graph Laplacian; Quantitative Structure-Activity Relationship; Ranking; Semi-supervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
  • Conference_Location
    Sichuan
  • Print_ISBN
    978-1-4673-0025-4
  • Type

    conf

  • DOI
    10.1109/FSKD.2012.6234360
  • Filename
    6234360